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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2025

Paired Neural Network for Matching Experimental and Predicted Infrared Spectra

Here, we present a novel machine learning (ML)-based scoring technique for determining the similarity between experimental and predicted infrared (IR) spectra for identification purposes. IR spectroscopy is a powerful technique used to identify the molecular structure and composition of a sample by measuring the unique vibrational frequency pattern of the molecule’s functional groups. Molecular identifications are often made by comparing experimental and reference spectra. However, the limited number of reference spectra available in spectral libraries can confound the identification process. Alternative identification procedures rely on in silico techniques to simulate spectra for a wide range of molecules. However, scoring spectral similarity between an experimental query and computationally predicted reference remains a significant challenge. Our proposed ML-based scoring technique overcomes these barriers by accurately and efficiently determining spectral similarity.

Neural Network↗

DOE OSTI2022

Photometric redshift estimation of galaxies in the DESI Legacy Imaging Surveys

ABSTRACT The accurate estimation of photometric redshifts plays a crucial role in accomplishing science objectives of the large survey projects. Template-fitting and machine learning are the two main types of methods applied currently. Based on the training set obtained by cross-correlating the DESI Legacy Imaging Surveys DR9 galaxy catalogue and the SDSS DR16 galaxy catalogue, the two kinds of methods are used and optimized, such as eazy for template-fitting approach and catboost for machine learning. Then, the created models are tested by the cross-matched samples of the DESI Legacy Imaging Surveys DR9 galaxy catalogue with LAMOST DR7, GAMA DR3, and WiggleZ galaxy catalogues. Moreover, three machine learning methods (catboost, Multi-Layer Perceptron, and Random Forest) are compared; catboost shows its superiority for our case. By feature selection and optimization of model parameters, catboost can obtain higher accuracy with optical and infrared photometric information, the best performance ($\rm MSE=0.0032$, σNMAD = 0.0156, and $O=0.88{{\ \rm per\ cent}}$) with g ≤ 24.0, r ≤ 23.4, and z ≤ 22.5 is achieved. But eazy can provide more accurate photometric redshift estimation for high redshift galaxies, especially beyond the redshift range of training sample. Finally, we finish the redshift estimation of all DESI Legacy Imaging Surveys DR9 galaxies with catboost and eazy, which will contribute to the further study of galaxies and their properties.

Astronomy & Astrophysics↗

DOE OSTI2021

Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Physics-informed transformation toward improving the machine-learned NLTE models of ICF simulations

The integration of machine-learning techniques into inertial confinement fusion (ICF) simulations has emerged as a powerful approach for enhancing computational efficiency. By replacing the costly nonlocal thermodynamic equilibrium (NLTE) model with machine-learning models, significant reductions in calculation time have been achieved. However, determining how to optimize machine-learning-based NLTE models in order to match ICF simulation dynamics remains challenging, underscoring the need for physically relevant error metrics and strategies to enhance model accuracy with respect to these metrics. Thus, we propose novel physics-informed transformations designed to emphasize energy transport, use these transformations to establish new error metrics, and demonstrate that they yield smaller errors within reduced principal-component spaces compared to conventional transformations. Published by the American Physical Society 2025

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1988

Expert systems for adaptive control of large space structures

It is expected that space systems for the future will evolve to structures of unprecedented size with associated extreme control requirements. A method is necessary that is sufficiently general to initiate stable control of a vehicle and subsequently learn the true nature of the structure. It is suggested that a suitable constructed expert system (ES) would be capable of learning by appending observations to a knowledge base. To verify that an ES can control a large space structure, numerical simulations of a simple structure subjected to periodic vibrations and the performance of a classical controllers were performed. The ES was then exercised to show its ability to truthfully mimic nominal control and to demonstrate its superiority to the classical controller, given sensor failures. An ES generating software package, The Intelligent Machine Model, was employed. It uses the pattern matching technique. Results of this program are discussed.

Gartrell, Charles F.↗

NASA NTRS2001

Metal Matrix Composite LOX Turbopump Housing Via Novel Tool-Less Net-Shape Pressure Infiltration Casting Technology

Metal matrix composites (MMC) offer relatively higher specific strength, specific stiffness, lower coefficient of thermal expansion (CTE) and lower density as compared with conventional alloys. These unique properties make them very attractive for aerospace turbomachinery applications where there is ever increasing emphasis to reduce weight and cost, and to increase engine performance. Through a joint effort between NASA and Metal Matrix Cast Composites, Inc., a complex liquid oxygen (LOX) compatible turbopump housing is being redesigned and manufactured from hybrid (particulate and fibers) Aluminum MMC. To this end, a revolutionary tool-less pressure infiltration casting technology is being perfected. Ceramic preforms for the composite are 3-dimensionally printed using a stereolithography file, acquired from a CAD model. The preforms are then invested into a refractory material and pressure infiltrated with liquid metal. After casting, the refractory material is washed away leaving behind a near net-shape composite part. Benefits of this process include increased composite uniformity, no mold machining, short time from design to part, properties matching traditional methods, ability to make previously impossible to manufacture parts and no size limitations with a newly developed joining technology. The results of materials, manufacturing and design optimizations, preform joining, and sub-element tests will be presented.

Shah, Sandeep↗

NASA NTRS2001

Metal Matrix Composite LOX Turbopump Housing Via Novel Tool-Less Net-Shape Pressure Infiltration Casting Technology

Metal matrix composites (MMC) offer relatively higher specific strength, specific stiffness, lower coefficient of thermal expansion (CTE) and lower density as compared with conventional alloys. These unique properties make them very attractive for aerospace turbomachinery applications where there is ever increasing emphasis to reduce weight and cost, and to increase engine performance. Through a joint effort between NASA and Metal Matrix Cast Composites, Inc., a complex liquid oxygen (LOX) compatible turbopump housing is being redesigned and manufactured from hybrid (particulate and Fibers) Aluminum MMC. To this end, a revolutionary tool-less pressure infiltration casting technology is being perfected. Ceramic preforms for the composite are 3-dimensionally printed using a stereolithography file, acquired from a CAD model. The preforms are then invested into a refractory material and pressure infiltrated with liquid metal. After casting, the refractory material is washed away leaving behind a near net-shape composite part. Benefits of this process include increased composite uniformity, no mold machining, short time from design to part properties matching traditional methods, ability to make previously impossible to manufacture parts and no size limitations with a newly developed joining technology. The results of materials, manufacturing and design optimizations, preform joining, and sub element tests will be presented.

Shah, Sandeep↗

NASA NTRS2002

Metal Matrix Composite LOX Turbopump Housing Via Novel Tool-less Net-Shape Pressure Infiltration Casting Technology

Metal matrix composites (MMC) offer relatively higher specific strength, specific stiffness, lower coefficient of thermal expansion (CTE) and lower density as compared with conventional alloys. These unique properties make them very attractive for aerospace turbomachinery applications where there is ever increasing emphasis to reduce weight and cost, and to increase engine performance. Through a joint effort between NASA and Metal Matrix Cast Composites, Inc., a complex liquid oxygen (LOX) compatible turbopump housing is being redesigned and manufactured from hybrid (particulate and fibers) Aluminum MMC. To this end, a revolutionary toolless pressure infiltration casting technology is being perfected. Ceramic preforms for the composite are 3-dimensionally printed using a stereolithography file, acquired from a CAD model. The preforms are then invested into a refractory material and pressure infiltrated with liquid metal. After casting, the refractory material is washed away leaving behind a near net-shape composite part. Benefits of this process include increased composite uniformity, no mold machining, short time from design to part, properties matching traditional methods, ability to make previously impossible to manufacture parts and no size limitations with a newly developed joining technology. The results of materials, manufacturing and design optimizations, preform joining, and sub-element tests will be presented.

Shah, Sandeep↗

NASA NTRS2023

Find My Astronaut Photo

An automated localization and georectification pipeline for astronaut photography

Machine Learning↗

NASA NTRS1982

Wire EDM for Refractory Materials

In an attempt to reduce fabrication time and costs, Wire Electrical Discharge Machine (Wire EDM) method was investigated as tool for fabricating matched blade roots and disk slots. Eight high-strength nickel-base superalloys were used. Computer-controlled Wire EDM technique provided high quality surfaces with excellent dimensional tolerances. Wire EDM method offers potential for substantial reductions in fabrication costs for "hard to machine" alloys and electrically conductive materials in specific high-precision applications.

Zellars, G. R.↗

DOE OSTI2013

Materials and Optimized Designs for Human-Machine Interfaces Via Epidermal Electronics

Thin, soft, and elastic electronics with physical properties well matched to the epidermis can be conformally and robustly integrated with the skin. Materials and optimized designs for such devices are presented for surface electromyography (sEMG). Furthermore, the findings enable sEMG from wide ranging areas of the body. The measurements have quality sufficient for advanced forms of human-machine interface.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Evaluating Automated Face Identity-Masking Methods with Human Perception and a Deep Convolutional Neural Network

Face de-identification (or “masking”) algorithms have been developed in response to the prevalent use of video recordings in public places. Here, we evaluated the success of face identity masking for human perceivers and a deep convolutional neural network (DCNN). Eight de-identification algorithms were applied to videos of drivers’ faces, while they actively operated a motor vehicle. These masks were pre-selected to be applicable to low-quality video and to maintain coarse information about facial actions. Humans studied high-resolution images to learn driver identities and were tested on their recognition of active drivers in low-resolution videos. Faces in the videos were either unmasked or were masked by one of the eight algorithms. When participants were tested immediately after learning (Experiment 1), all masks reduced identification, with six of eight masks reducing identification to extremely poor performance. In a second experiment, two of the most effective masks were tested after a delay of 7 or 28 days. The delay did not further reduce identification of the masked faces. In all masked conditions, participants maintained stringent decision criteria, with low confidence in recognition, further indicating the effectiveness of the masks. Next, the DCNN performed an identity-matching task between high-resolution images and masked videos—a task analogous to that done by humans. The pattern of accuracy for the DCNN mirrored some, but not all, aspects of human performance, highlighting the need to test the effectiveness of identity masking for both humans and machines. The DCNN was also tested on its ability to match identity between masked and unmasked versions of the same video, based only on the face. DCNN performance for the eight masks offers insight into the nature of the information in faces that is coded in these networks.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1986

Matching Vibration Testing to "Real-World" Conditions

Vibration spectrum of test machine adjusted to that observed in operation. Test specimen placed in test fixture and attached to shaker. Shaker initially operated at one-quarter full level of input spectrum to prevent overloading. Short vibration test run, and specimen response compared to control spectrum. Imput spectrum then adjusted until response resembles control spectrum.

Olsen, Albert D., Jr.↗

DOE OSTI2020

CMDV (CM)4 Project - University of Washington contribution. Final report

The original goal of this project was to diagnose and improve CLUBB, the turbulence and cloud fraction parameterization used in DOE’s E3SM model. For this purpose, we planned to use large-eddy simulation (LES) and ARM observations from the NE Pacific Ocean and the SGP site, in coordination with ARM’s LASSO program. It was determined that for these cloud regimes, many of the turbulent ‘moment closures’ which underlie CLUBB’s mathematical formulation are inconsistent with LES, which is an appropriate benchmark for testing this. The research assistant found that these closures could be more accurately formulated using machine learning using the LES as a training dataset. However the resulting parameterization proved to quickly drift away from physical plausibility. A novel machine-learning based boundary layer parameterization called MARBLE based on matching the time evolution of a parameterized cloud-topped boundary layer to reanalysis was then developed and published.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Observational evidence for groundwater influence on crop yields in the United States

As climate change shifts crop exposure to dry and wet extremes, a better understanding of factors governing crop response is needed. Recent studies identified shallow groundwater—groundwater within or near the crop rooting zone—as influential, yet existing evidence is largely based on theoretical crop model simulations, indirect or static groundwater data, or small-scale field studies. Here, we use observational satellite yield data and dynamic water table simulations from 1999 to 2018 to provide field-scale evidence for shallow groundwater effects on maize yields across the United States Corn Belt. We identify three lines of evidence supporting groundwater influence: 1) crop model simulations better match observed yields after improvements in groundwater representation; 2) machine learning analysis of observed yields and modeled groundwater levels reveals a subsidy zone between 1.1 and 2.5 m depths, with yield penalties at shallower depths and no effect at deeper depths; and 3) locations with groundwater typically in the subsidy zone display higher yield stability across time. We estimate an average 3.4% yield increase when groundwater levels are at optimum depth, and this effect roughly doubles in dry conditions. Groundwater yield subsidies occur ~35% of years on average across locations, with 75% of the region benefitting in at least 10% of years. Overall, we estimate that groundwater-yield interactions had a net monetary contribution of approximately $10 billion from 1999 to 2018. This study provides empirical evidence for region-wide groundwater yield impacts and further underlines the need for better quantification of groundwater levels and their dynamic responses to short- and long-term weather conditions.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2011

Provisioning a Multi-Tiered Data Staging Area for Extreme-Scale Machines

Massively parallel scientific applications, running on extreme-scale supercomputers, produce hundreds of terabytes of data per run, driving the need for storage solutions to improve their I/O performance. Traditional parallel file systems (PFS) in high performance computing (HPC) systems are unable to keep up with such high data rates, creating a storage wall. In this work, we present a novel multi-tiered storage architecture comprising hybrid node-local resources to construct a dynamic data staging area for extreme-scale machines. Such a staging ground serves as an impedance matching device between applications and the PFS. Our solution combines diverse resources (e.g., DRAM, SSD) in such a way as to approach the performance of the fastest component technology and the cost of the least expensive one. We have developed an automated provisioning algorithm that aids in meeting the checkpointing performance requirement of HPC applications, by using a least-cost storage configuration. We evaluate our approach using both an implementation on a large scale cluster and a simulation driven by six-years worth of Jaguar supercomputer job-logs, and show that our approach, by choosing an appropriate storage configuration, achieves 41.5% cost savings with only negligible impact on performance.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2013

Evolutionary Tuning of Building Models to Monthly Electrical Consumption

Building energy models of existing buildings are unreliable unless calibrated so they correlate well with actual energy usage. Calibrating models is costly because it is currently an art which requires significant manual effort by an experienced and skilled professional. An automated methodology could significantly decrease this cost and facilitate greater adoption of energy simulation capabilities into the marketplace. The Autotune project is a novel methodology which leverages supercomputing, large databases of simulation data, and machine learning to allow automatic calibration of simulations to match measured experimental data on commodity hardware. This paper shares initial results from the automated methodology applied to the calibration of building energy models (BEM) for EnergyPlus (E+) to reproduce measured monthly electrical data.

Garrett, Aaron↗

DOE OSTI2021

Nuclear Safeguards: Feature Extraction for Machine Learning Enrichment Analysis of UF 6 Cylinders

With the increasing international interest in using nuclear material as sources of energy, comes the growing concern that we may see an increase in proliferation threats. Uranium hexafluoride (UF 6 ) is used in the nuclear fuel cycle for uranium enrichment. Inspectors from the International Atomic Energy Agency (IAEA) monitor the enrichment levels of UF 6 stored in transportation cylinders, to ensure the enrichments match that of a facility operator’s declarations. However, only a characteristic subset of UF 6 cylinders can be measured by inspectors from the IAEA during these inspections. In turn, the inspector may not identify a cylinder whose enrichment levels do not match the facility declarations. Therefore, our team attempts to develop a machine learning network that could determine the enrichment percentage of cylinders as they enter and exit facilities. The machine learning model must be robust against spectral variations due to factors that are internal and external to the UF 6 cylinder. Such factors include but are not limited to the speed and distance of the moving vehicle, cylinder type, cylinder orientation, and fill level. Many features in the spectral continuum can be used to identify and correct for some of these variations. As a consequence, the model must extract numerous features in the continuum. We investigate new approaches in continuum subtraction to verify enrichment percentage: interpolation and extrapolation of lines at the notable characteristic spectral peaks. It was determined that there was no clear answer on which method proved superior, therefore the decision was made to implement both new methods into the current codebase. We currently rely on synthetic training and testing data to analyze the results and fine tune our models but hope to test the system on measured data soon.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗